Artificial Intelligence: Glossary - NN/G

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August 21, 2026 2026-08-21
AI conversations run on a lot of specialized language. Use this glossary to look up a term you have run into, or to build a working vocabulary before you need one. Definitions follow how the terms are used in AI products and UX work; vendors and researchers sometimes use them differently. Many entries link to NN/G articles that go deeper; for a guided tour of the topic, start with our study guide on how AI works and how users think about it.
Look up the term you need in the table below, search this page with your browser’s Find command, or read straight through. Terms run alphabetically down each column.
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A system that can use a model and tools to carry out multiple steps toward a goal, evaluate its progress, and decide what to do next. The product around it controls its tools, permissions, stopping rules, and opportunities for human review.
A description of how much latitude a system has between prompts. An agentic system can choose several steps and use tools toward a goal without being told each move. Autonomy is a matter of degree, so calling a product an agent says little by itself about how much autonomy it actually has.
The work of turning AI models into reliable products: supplying context, designing prompts, connecting tools, evaluating behavior, deploying, monitoring, and adding guardrails. Depending on the team, it may also extend to preparing data and adapting models.
Low-quality, mass-produced AI-generated content: text, images, or code that is plausible on the surface but careless, generic, or unverified. Guarding against it with quality bars and human review is a design and governance concern.
Making false, misleading, or exaggerated claims about whether a product uses AI, how it uses it, or what it can do. It can erode user trust when the capability does not match the claim.
A step-by-step procedure or set of rules for solving a problem or performing a task. In AI, algorithms train models, search for solutions, make predictions, retrieve information, and choose actions.
The effort to make AI systems behave in accordance with human values, intentions, and ethical guidelines. A central design challenge is making systems that do what users want while avoiding harmful or unintended outcomes.
Attributing human qualities such as feeling, intent, or humanlike understanding to an AI system because of the way it communicates. It can make a product feel approachable, and it can just as easily lead users to trust the system further than its reliability supports. First-person language, apologies, and other humanlike cues may strengthen the effect, often called the “ELIZA effect” after a 1966 chatbot.
A hypothetical AI with broad, flexible capability across many intellectual tasks. No current system is generally accepted as AGI, and there is no agreed definition or test for it. Today’s general-purpose models handle many tasks, but unevenly.
Broadly, any computer system designed to perform tasks that typically require human intelligence, such as learning from examples, recognizing patterns, understanding language, or making decisions. Also, the subfield of computer science concerned with researching and developing tools that enable computers to act “intelligently.”
The tendency to favor an automated system’s output and to scrutinize it less than you would a person’s, even when it is wrong. Automation bias is one form of overreliance; overreliance is broader and can arise without automation-specific bias. Introducing friction at high-stakes moments and providing easy ways for users to verify or override a result help prevent automation bias.
Systematic skew in data, models, outputs, organizational processes, or human use, particularly skew that unfairly disadvantages certain people, groups, or viewpoints. Bias can enter at any stage of an AI system’s lifecycle. Testing can reveal it, though no test establishes that a system is free of it; addressing it may require changes to the data, the model, the product, or the process around them.
A prompting technique that uses an instruction or worked examples to prompt a sequence of intermediate reasoning steps before a final answer. It can improve performance on many multistep tasks, but the generated steps are not guaranteed to faithfully explain how the model arrived at its answer.
The amount of computation required to complete a task, often measured in operations such as floating-point operations (FLOPs).
Everything supplied or made available to a model for a single request: the prompt, the system prompt, selected conversation history, retrieved documents, stored memory, and the results of any tools it has called. In most current assistant products, the surrounding application or service stores conversational state and supplies or references it when relevant. Context is what is available for one request; memory is retained information that may later become part of that context. Choosing deliberately what goes into the context is sometimes called context engineering.
The maximum amount of tokenized information a model can take into account at once. It is usually not just the user’s text: conversation history, retrieved documents, tool results, images, and some or all of the generated response are typically counted against it. A large window lets a model reference more, though not everything in a long window influences the answer equally.
A collection of examples used to train or evaluate an AI model. Datasets are the foundation of machine learning; model quality depends heavily on the quality, size, and representativeness of its data.
An approach to machine learning that uses networks with many computational layers, and is often used